Anomaly Detection Blueprint for Imbalanced Data
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Solution Overview
Problem
Current anomaly detection methods are inefficient and inaccurate due to reliance on supervised machine learning, which struggles with imbalanced training data, lack of ground truth, high data volumes, and changing user behavior, particularly in cases involving free text data and multivariate datasets.
Innovation Solution
The development of an anomaly detection method that generates a blueprint for predicting anomalous data samples based on feature types and dataset size, incorporating tasks like missing value imputation, feature engineering, and anomaly scoring, using both unsupervised and supervised approaches to identify and explain anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised machine learning is used for anomaly detection, then predictive models can be trained to make predictions based on known outcomes, but the method becomes inefficient and inaccurate when training data is imbalanced or lacks ground truth
Solution Approach 1:
The patent inverts the traditional supervised learning approach by using unsupervised learning to detect anomalies without requiring labeled training data. Instead of training models on known anomalous outcomes, the system learns normal patterns and identifies deviations, thereby eliminating the dependency on imbalanced or incomplete ground truth data while maintaining detection reliability
Solution Approach 2:
The system dynamically adjusts detection parameters based on data characteristics and context. By changing parameters such as anomaly score thresholds, detection sensitivity, and feature weighting in response to data volume, complexity, and distribution, the system maintains high accuracy across diverse datasets without requiring manual retraining or labeled examples
2Adaptability or versatility
If traditional anomaly detection methods are used, then simple data types can be processed, but complex multivariate data with free text becomes computationally costly and time consuming
Solution Approach 1:
The patent segments the anomaly detection process into distinct modules: data preprocessing, feature extraction, anomaly scoring, and explanation generation. Each module handles specific data types separately, allowing efficient processing of complex multivariate data with free text by breaking down the computational tasks into manageable segments that can be executed independently and optimized individually
Solution Approach 2:
The system introduces intermediate representations and transformations that bridge raw complex data and final anomaly detection. By converting free text and multivariate data into standardized feature vectors and anomaly scores through intermediate processing layers, the system reduces computational complexity while maintaining the ability to handle diverse data types
3Loss of information
If anomaly detection processes provide rationale for identification, then system improvements can be implemented, but the process becomes more complex and time consuming
Solution Approach 1:
The system performs preliminary feature selection and importance ranking during the training phase, preparing explanations in advance. By pre-identifying which features contribute most to anomaly detection and preparing explanation templates, the system can quickly generate rationales during inference without adding significant processing time, thus maintaining both explanation quality and efficiency
Data Source
AI summary
Methods for detection of anomalous data samples from a plurality of data samples are provided. In some embodiments, an anomaly detection procedure that includes a plurality of tasks is executed to identify the anomalous data samples from the plurality of data samples.